An Empirical Study and Open Testbed for Federated Fine-Tuning of Vision-Language-Action Models
cs.RO
Submitted: 2026-09-19
Updated: 2026-09-27
Terminology
Sources
- SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics
- GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
- Flower: A Friendly Federated Learning Research Framework
- Federated Learning with Personalization Layers
- LIBERO-PRO: Towards Robust and Fair Evaluation of Vision-Language-Action Models Beyond Memorization
- ${\pi}_{0.7}$: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities
- RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
Related papers
- FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
- MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
- RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
- HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
- APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
- Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving